Long-term home monitoring of hypermotor seizures by patient-worn accelerometers

Vel, Anouk Van de; Cuppens, Kris; Bonroy, Bert; Milosevic, Milica; Huffel, Sabine Van; Vanrumste, Bart; Lagae, Lieven; Ceulemans, Berten

doi:10.1016/j.yebeh.2012.10.006

Cited by 51 publications

(32 citation statements)

References 19 publications

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“…Focal dyscognitive seizures without motor phenomena and absence seizures were not detected [19,83,84]. Sensitivity ranges between 16-100%; one study had a FDR of 0.2/day [83,84,86,87,[89][90][91][92]. Seizures were detected 9-60 seconds after seizure onset [84,87].…”

Section: Accelerometry (Acm)mentioning

confidence: 99%

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Automated seizure detection systems and their effectiveness for each type of seizure

Ulate‐Campos

Coughlin

Gaínza‐Lein

et al. 2016

Seizure

189

166

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Epilepsy affects almost 1% of the population and most of the approximately 20-30% of patients with refractory epilepsy have one or more seizures per month. Seizure detection devices allow an objective assessment of seizure frequency and a treatment tailored to the individual patient. A rapid recognition and treatment of seizures through closed-loop systems could potentially decrease morbidity and mortality in epilepsy. However, no single detection device can detect all seizure types. Therefore, the choice of a seizure detection device should consider the patient-specific seizure semiologies. This review of the literature evaluates seizure detection devices and their effectiveness for different seizure types. Our aim is to summarize current evidence, offer suggestions on how to select the most suitable seizure detection device for each patient and provide guidance to physicians, families and researchers when choosing or designing seizure detection devices. Further, this review will guide future prospective validation studies.

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Section: Accelerometry (Acm)mentioning

confidence: 99%

“…The signal is recorded by means of a three-axis motion/accelerometer sensor, a microprocessor, and a small, rechargeable battery, usually placed on a limb [83,84,[86][87][88]. The main challenge is to differentiate seizures from normal, daily, repetitive movements [19,89].…”

Section: Accelerometry (Acm)mentioning

confidence: 99%

Automated seizure detection systems and their effectiveness for each type of seizure

Ulate‐Campos

Coughlin

Gaínza‐Lein

et al. 2016

Seizure

189

166

View full text Add to dashboard Cite

show abstract

“…On the other hand, there are solutions where the gathered data is stored for data analytics [39,40] or even for future use [41][42][43][44]. Finally, complete CC solutions have been also tackled in the literature, including not only the data storage and visualization but also the modelling and classification of the current state of affairs [7,31,32,[45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

“…ACM, gyroscopes, and magnetometers were proposed in [9] as the BSN, taking advantage of the local Smartphone for analyzing the data and sending e-mails to the medical staff with the patient position. Similar works were presented in [47,48]. LabView has been used for developing a solution as well [35] by using an Arduino ACM sensor to generate alarms that are transmitted to the medical service.…”

Section: Introductionmentioning

confidence: 99%

An IoT Platform for Epilepsy Monitoring and Supervising

et al. 2017

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Epilepsy is a chronic neurological disorder with several different types of seizures, some of them characterized by involuntary recurrent convulsions, which have a great impact on the everyday life of the patients. Several solutions have been proposed in the literature to detect this type of seizures and to monitor the patient; however, these approaches lack in ergonomic issues and in the suitable integration with the health system. This research makes an in-depth analysis of the main factors that an epileptic detection and monitoring tool should accomplish. Furthermore, we introduce the architecture for a specific epilepsy detection and monitoring platform, fulfilling these factors. Special attention has been given to the part of the system the patient should wear, providing details of this part of the platform. Finally, a partial implementation has been deployed and several tests have been proposed and carried out in order to make some design decisions.

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“…Moreover, a multitude of algorithms for online bio-signal extraction and automatic pathology detection, are ready to be integrated in such a medical body sensor network but are in need for high performance, ergo they consume abundant energy [10][11][12]. On the other hand, the patient's comfort level is affected by the device's dimensions.…”

Section: Introductionmentioning

confidence: 99%

Contactless energy transfer at the bedside featuring an online power optimization strategy

Clercq

Puers

2014

Sensors and Actuators A: Physical

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a b s t r a c tThis paper introduces the principle of contactless energy transfer to wearable bedside electronics. As a proof of concept, a platform containing a matrix of magnetic energy transmitters has been developed and integrated into a mattress. This system is able to feed one or more wearable sensor nodes, attached to the patient, with the purpose of wirelessly recording vital signals over an extended period of time. Each of these power transmitters is designed to transfer up to 10 mW of power over a maximal distance of 300 mm. Moreover, the platform embeds online optimization software to deal with vertical as well as horizontal sensor node movements. The position and orientation of each freely moving sensor node can be dynamically determined and a local magnetic field, with a matching radiated power level, can be selectively induced in the area where the sensor nodes are positioned. This approach therefore limits the radiation exposure to the patient to the minimum viable level. The prototype is successfully tested in a laboratory set-up.

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Long-term home monitoring of hypermotor seizures by patient-worn accelerometers

Cited by 51 publications

References 19 publications

Automated seizure detection systems and their effectiveness for each type of seizure

Automated seizure detection systems and their effectiveness for each type of seizure

An IoT Platform for Epilepsy Monitoring and Supervising

Contactless energy transfer at the bedside featuring an online power optimization strategy

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